{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:FBH5ONFTRKQAST7UQP72S62K7H","short_pith_number":"pith:FBH5ONFT","schema_version":"1.0","canonical_sha256":"284fd734b38aa0094ff483ffa97b4af9dde26185818e230af9c0c85f96cf29fe","source":{"kind":"arxiv","id":"1807.09237","version":1},"attestation_state":"computed","paper":{"title":"Hierarchical infinite factor model for improving the prediction of surgical complications for geriatric patients","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Elizabeth Lorenzi, Katherine Heller, Ricardo Henao","submitted_at":"2018-07-24T17:15:01Z","abstract_excerpt":"We develop a hierarchical infinite latent factor model (HIFM) to appropriately account for the covariance structure across subpopulations in data. We propose a novel Hierarchical Dirichlet Process shrinkage prior on the loadings matrix that flexibly captures the underlying structure of our data across subpopulations while sharing information to improve inference and prediction. The stick-breaking construction of the prior assumes infinite number of factors and allows for each subpopulation to utilize different subsets of the factor space and select the number of factors needed to best explain "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1807.09237","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-07-24T17:15:01Z","cross_cats_sorted":[],"title_canon_sha256":"9fee0abc896772071b4e6544f6278d5b5175647e10d0fc55ec6fda453c68e7fe","abstract_canon_sha256":"3e3af1987da970e408f825392f2ac53c89542baa3bf74eaa5bf355fdf9efa0e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:56.305857Z","signature_b64":"EC8up2zfIYavbUBhZjtHxS76r/TCiynfRbY1+P/sIDRO3u8lhjoUQQagiMRb3skJMAfEb4eUGNP0zfwl7/xcCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"284fd734b38aa0094ff483ffa97b4af9dde26185818e230af9c0c85f96cf29fe","last_reissued_at":"2026-05-18T00:09:56.305162Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:56.305162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical infinite factor model for improving the prediction of surgical complications for geriatric patients","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Elizabeth Lorenzi, Katherine Heller, Ricardo Henao","submitted_at":"2018-07-24T17:15:01Z","abstract_excerpt":"We develop a hierarchical infinite latent factor model (HIFM) to appropriately account for the covariance structure across subpopulations in data. We propose a novel Hierarchical Dirichlet Process shrinkage prior on the loadings matrix that flexibly captures the underlying structure of our data across subpopulations while sharing information to improve inference and prediction. The stick-breaking construction of the prior assumes infinite number of factors and allows for each subpopulation to utilize different subsets of the factor space and select the number of factors needed to best explain "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.09237","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1807.09237","created_at":"2026-05-18T00:09:56.305279+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.09237v1","created_at":"2026-05-18T00:09:56.305279+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.09237","created_at":"2026-05-18T00:09:56.305279+00:00"},{"alias_kind":"pith_short_12","alias_value":"FBH5ONFTRKQA","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_16","alias_value":"FBH5ONFTRKQAST7U","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_8","alias_value":"FBH5ONFT","created_at":"2026-05-18T12:32:22.470017+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FBH5ONFTRKQAST7UQP72S62K7H","json":"https://pith.science/pith/FBH5ONFTRKQAST7UQP72S62K7H.json","graph_json":"https://pith.science/api/pith-number/FBH5ONFTRKQAST7UQP72S62K7H/graph.json","events_json":"https://pith.science/api/pith-number/FBH5ONFTRKQAST7UQP72S62K7H/events.json","paper":"https://pith.science/paper/FBH5ONFT"},"agent_actions":{"view_html":"https://pith.science/pith/FBH5ONFTRKQAST7UQP72S62K7H","download_json":"https://pith.science/pith/FBH5ONFTRKQAST7UQP72S62K7H.json","view_paper":"https://pith.science/paper/FBH5ONFT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.09237&json=true","fetch_graph":"https://pith.science/api/pith-number/FBH5ONFTRKQAST7UQP72S62K7H/graph.json","fetch_events":"https://pith.science/api/pith-number/FBH5ONFTRKQAST7UQP72S62K7H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FBH5ONFTRKQAST7UQP72S62K7H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FBH5ONFTRKQAST7UQP72S62K7H/action/storage_attestation","attest_author":"https://pith.science/pith/FBH5ONFTRKQAST7UQP72S62K7H/action/author_attestation","sign_citation":"https://pith.science/pith/FBH5ONFTRKQAST7UQP72S62K7H/action/citation_signature","submit_replication":"https://pith.science/pith/FBH5ONFTRKQAST7UQP72S62K7H/action/replication_record"}},"created_at":"2026-05-18T00:09:56.305279+00:00","updated_at":"2026-05-18T00:09:56.305279+00:00"}